
Conv LSTM/GRU [1]}: a combination of convolutional layers and recurrent layers.

Multi-scale CNN [2]}: Multiple convolutional layers with different kernel size concatenated in parallel for extracting patterns in various receptive fields.

TCN [3]}, [4]}: Temporal convolutional network with dilation designed to handle long input sequence.

RAIM [5]}: A recurrent attentive hierarchical model designed for multimodal patient data.

SEHM(LSTM/GRU): Our proposed model with a multi-head kernelized local attention layer and an RNN layer on the top.

